Librarians and Related Information Professionals

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Librarians and Related Information Professionals (ISCO-08 2622) score an average of 0.55 on a 0–1 exposure scale — more exposed than about 93% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Devising and implementing schemes and conceptual models for the storage, organization, classification and retrieval of information;”

Scores 0.61 on the 2025 scale. The task of devising and implementing schemes and conceptual models for the storage, organization, classification, and retrieval of information involves significant data management, organization, and potentially creative problem-solving to create efficient systems. Generative AI can assist in analyzing data patterns, suggesting categorizations, and automating routine processes like data entry and retrieval. However, this task still requires nuanced human judgment to ensure that the models are contextually and strategically aligned with the organization's needs and goals. Looking at semantically related tasks, such as creating and managing content management systems (0.7), and maintaining digital records (0.575 to 0.65), we can see similar dependencies on structured information management, but with a need for human oversight to handle complexities and ensure strategic alignment. The task is more complex than straightforward documentation or archiving tasks (around 0.575 to 0.625) due to its requirement to devise new systems, suggesting a slightly lower score than fully automatable content management tasks but higher than routine archiving processes. Therefore, considering the technological infrastructure of Poland, the adjusted score of 0.58 reflects the potential of AI to streamline but not fully automate this task due to the required level of human expertise and decision-making involved.

Moving fastest, 2023 → 2025

“Preparing scholarly papers and reports;”

Model capability on this task changed by +0.10 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2622, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

No U.S. role resolves through the crosswalk for this occupation. Search the encyclopedia for the closest match →

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Librarians and Related Information Professionals sit at the 93rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Librarians and Related Information Professionals rank in the 93rd percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.10 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Devising and implementing schemes and conceptual models for the storage, organization, classification and retrieval of information;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Librarians and Related Information Professionals". https://singulariki.com/gradient/2622-librarians-and-related-information-professionals.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.